Powder Ejection in Binder Jetting Additive Manufacturing

Hi there! My name is Sean Wang, and I’m a rising sophomore at Northwestern studying Materials Science and Engineering. I worked with Tao Sun, Niranjan Parab, and Cang Zhao in the X-Ray Science Division on data analysis of powder ejection in binder jetting additive manufacturing.

Additive manufacturing (AM), commonly referred to as 3D printing, is the process of creating parts from CAD by fusing thin layers of material together. This is typically done using heat, through either Fused Deposition Modeling in which filament is melted in a thin string and layered to create a part, or Powder Bed Fusion (where a laser scans a powder bed and melts powder into layers). These techniques utilize high amounts of heat that can lead to the formation of residual stress and create defects.

An alternative AM technique known as Binder Jetting uses no heat during the process. The process is like printing with ink; a print head moves across the powder bed and drops binder, bonding the powder together to form solid layers. The solid part is later treated with heat, if needed, and finished. Because the binder jetting process does not melt powder, materials that are difficult to melt (such as ceramics) or are sensitive to heat can be utilized.

However, one of the problems that limits the use of binder jetting is powder ejection when the binder drops onto the powder bed. The ejected powder can land on different areas of the bed and lead to powder bed depletion. Understanding how different types of powdered materials eject from the bed will help shape manufacturing parameters and improve the quality of parts created. The Advanced Photon Source lets us take high speed x-ray images that we can analyze for insights into the binder jetting process.

Figure 1: Short x-ray image sequence of SS 316 30µm binder jetting.

During my time at Argonne, I looked at 4 types of powder particles: Stainless Steel 316 (30µm and 9µm), Al2O3(32µm), and Silicon (9µm). I imported 10 x-ray image sequences into the ImageJ software to threshold them and filter out spurious features. By isolating the powder particles within a set frame, we can use the built-in particle tracker in ImageJ to count the number of airborne particles and calculate the area in the frame they occupy.

Figure 2: Processed image sequence of SS 316 30µm ready for particle tracking.

 

Figure 3: ImageJ tracking function highlights each particle. The software gives information about the count of particles and their combined area in each frame.

Data and Analysis

Figure 4: Data graphs showing count and area over time.

Several insights can be learned from the data provided by ImageJ.

  • SS 316 has the most particle ejection out of the different powders. SS 316 30µm is most likely to cause significant powder bed depletion due to the large volume of particles.
  • Al2O3 has less ejection than SS 316 because of mechanical interlocking, where the particles grasp onto each other and prevent ejection.
  • Si 9µm had little ejection, but the ejected powder clumped together in large chunks when ejected. This can lead to significant powder bed depletion in localized areas.
  • Area data for Si 9µm not available because ImageJ was unable to process the image and track particles. Particle count was done manually.

Some future work to further the development of binder jetting would be to incorporate a machine learning algorithm to automatically process images and track particles. Also, in my processed images some particles seemed to disappear for a few frames or join with other particles when particles cross each other (a limitation of the 2-dimensional nature of the image sequences). The particle tracking feature in ImageJ is unable to account for these occurrences, lowering the accuracy of the data and requiring adjustments to be made for each individual sequence. Automating these processes would let researchers test and analyze a large variety of image sequences and gain insight into this developing process.

Figure 7: Processed image that suffers from disappearing and cross-path particles.

 

Additive manufacturing has the potential to revolutionize the way we design and create by reducing wasted time, energy, and resources from traditional manufacturing processes. I enjoyed assisting the X-Ray Science Division with their research into in-situ characterization of multiple additive manufacturing processes using high-speed x-ray techniques, and am extremely thankful to Dr. Jennifer Dunn, Tao Sun, Niranjan Parab, and Cang Zhao for the opportunity to work at Argonne National Laboratory this summer.

Flame Spray Pyrolysis: A Novel Powder Production Technology

Hello, my name is Vrishank and I’m a materials science and engineering major working on Flame Spray Pyrolysis this summer under Joe Libera, Nikola Ferrier and Jakob Elias, along with Ignacio Gonzales who worked with coding. Flame Spray Pyrolysis is a method of producing nanopowders by burning specific precursor solutions in a continuous flame. While the method holds promise for the scalable and continuous production of nanoparticles, it is possible to optimize the conditions for production in order to fine tune the final products.

 

The search for sustainable and scalable nanopowder production is of the utmost important in the face of the global energy crisis. Their high surface area-to-volume ratio is the key to optimizing industry processes where FSP products such as LLZO and TEOS find use as catalysts and electrolytes. One advantage of FSP is that it allows for the fine tuning of nanoparticle morphologies and will allow for Argonne to benchmark industry procedures for different compounds and properties.

 

This summer I worked closely with Joe Libera, understanding the process, trimming data using the in-lab data view program and analyzing the optical emission spectroscopy and Scanning Mobility Particle Sizer data. The main obstacle we encountered was the little resource and literature we had on the correlations between the optical emission spectrum and the product and thus decided the best route for us to go was to develop analytical tools to help deconstruct the FSP OES data.

-Vrishank Menon

 

 

I’m Ignacio Gonzales, rising junior who is majoring in a Mechanical Engineering and Manufacturing and Design Engineering. Over the summer I worked, analysing data at the Flame Spray Pyrolysis (FSP) project under Jakob Elias.  The FSP is a new method of production of nanomaterials that Joe Libera has been developing at the Argonne National Laboratory. The benefits of this method is that it will enable a continuous production of nanomaterials (as opposed to bulk production) and it will costs less than current methods of production. Currently the FSP is able to produce nanomaterials, however these materials are raw; their shape, size and agglomerate structure are not controlled. The project consists in optimizing the conditions, of the FSP in order to be able to produce a final product with full control of the outcome.

 

I was assigned to the computational side of this project along with David MacCumber. Joe Libera and his team, had been conducting various tests with the FSP using various concentrations both  LLZO and TEOS particles. From this Trials, there was a lot of data to work with, including Optical Emission Spectroscopy (OES) and Scanning Mobility Particle Sizer (SMPS) data. I mainly worked on deconstructing the OES data in order to obtain, specific features  such as peak, height, width, area, equations for each broadband, etc. To achieve this, I worked closely with Vrishank Menon, and intern in the experimental side, he acted as a bridge between the experimental and computational side of this project and helped us understand the relevance of the data . Additionally I used various toolkits and packages in Python, such as RamPy and SciPy to perform a more accurate analysis . On the future, features will be used to create a Neural Net and for a Machine Learning platform in which using additional data such as a APS X-ray analysis on the nanomaterial, we can predict the properties of the nanomaterial produced.

-Ignacio Gonzales

Bioprocessing for Additive Manufacturing

Hello, my name is Patricia Lohman and I am a rising sophmore studying material science and engineering at Northwestern University. I work under Meltem Urgun-Demirtas and Patty Campbell on in a bioprocess and reactive separations group. This summer I was tasked with bioprocessing for additive manufacturing or making 3D printable pastes made of food waste.

For much of the summer performed literature searches to compile a list of procedures for making biofilms out of different food waste. This included food waste with three different types of base materials. Vegetable and fruit waste (peels, shells, and seeds) made of cellulose, egg shells made of calcium carbonate, and shrimp shells from which chitosan could be extracted from. After the search, I worked in lab recreating the biopolymers found in the studies. I started with fruit and vegetable waste materials. To do so the process involved digesting dried plant waste material in dilute acid and casting the resulting mixture. In particular, spinach waste produced a flexible film.

Figure 1: Spinach waste biofilm

The egg shell biomaterials began with dried and fine egg shell powder and was mixed with a binder solution until a clay-like paste was produced. The clay could be molded easily and held its shape.

Figure 2: Egg shell paste

The above egg shell paste used less than 90 µm egg shell powder and sucrose water in a 1:1 ratio as a binder.

Shrimp shells do not have chitosan directly available. The shells once demineralized and deproteinized start with chitin. Chitin then undergoes a deacetylation reaction with concentrated NaOH at high temperatures to remove the acetyl group and convert chitin into chitosan.

Figure 3: Chitin to chitosan deacetylation

The effectiveness of the reaction is key in determining the crystallinity, hydrophilicity, degradation, and mechanical properties of chitosan biomaterials. The target was to produce chitosan with a degree of deacetylation of 60% or greater. In lab I began working with pure purchased chitin and performing the deacetylation process under different conditions and measuring the degree of deacetylation. I plan to work on changing the concentration of NaOH, temperature, and conducting the reaction in an inert atmosphere to achieve that degree of deacetylation. After isolation, chitosan can be added to a number of organic solvents to form an extrudable paste.

Once the biopolymers were replicated, I planned on manipulating process parameters to achieve a consistency of paste that could be extruded by the Discov3ry extruder attachment, made especially for pastes, with an Ultimaker 3D printer.

Making bioplastics out of waste material is not only a novel idea, its an essential one. Food and plastic waste are glaring problems that have vast detrimental consequences on the planet. Finding alternatives to the materials used everyday is a good first step to tackling the issue. This project does a great job of addressing waste issues and providing exciting advances for additive manufacturing.  I am very grateful that I was able to work on an impactful project and am excited to see where it goes. A special thank you to my PI’s and Dr. Jennifer Dunn for all their help this summer.